Enhancing the Readability of Online Pediatric Cataract Education Materials: A Comparative Study of Large Language Models.

Purpose: The purpose of this study was to assess large language models (LLMs) for enhancing the readability of online patient education materials (PEMs) on pediatric cataracts through multilingual adaptation, content retrieval, and prompt engineering.

Methods: This study included 103 PEMs presented in different languages and retrieved from diverse resources. Three LLMs (ChatGPT-4o, Gemini 2.0, and DeepSeek-R1) were used for content improvement. Readability was assessed for both the original and converted PEMs with multiple formulas. Different prompt engineering strategies for LLMs were also tested in this study.

Results: The PEMs directly generated by LLMs exceeded a 10th grade reading level. Compared to a traditional Google search, LLMs' web browsing feature provided online PEMs with better characteristics and a higher reading level. Original PEMs from Google showed significantly improved readability after LLM conversion, with DeepSeek-R1 achieving the greatest reduction in reading level from 10.59 ± 2.20 to 7.01 ± 0.91 (P < 0.001). Prompt engineering also showed statistically significant results in their effects on LLM conversion, and Zero-shot-Cot (APE) successfully achieving target readability below the sixth grade reading level. Besides, the LLMs' simplified Chinese conversion, as well as the LLMs conversion of other original Chinese PEMs, both showed that they meet the recommended standards for reading levels in multiple dimensions.

Conclusions: LLMs can significantly enhance the readability of multilingual online PEMs on pediatric cataract. Combining it with web browsing and prompt engineering can further optimize outcomes and advance patient education.

Translational relevance: This study links LLMs with patient education and demonstrates their potential to significantly improve the readability of online PEMs.